Editors’ Introduction: Best Papers from the 18th International Conference on Cognitive Modeling
Bibliographic record
Abstract
Abstract The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the four best representative papers on this work from our 18th meeting, ICCM 2020, which was also the first meeting to be held virtually. Two of these papers develop novel techniques for building larger and more complex models using Reinforcement Learning and Learning By Instruction, respectively. The other two show how cognitive models connect to neuroscience, drawing on details of the hippocampus and cerebellum to constrain and explain the cognitive processes involved in memory and conditioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.045 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".